A Localization Approach to Improve Iterative Proportional Scaling in Gaussian Graphical Models

Abstract

We discuss an efficient implementation of the iterative proportional scaling procedure in the multivariate Gaussian graphical models. We show that the computational cost can be reduced by localization of the update procedure in each iterative step by using the structure of a decomposable model obtained by triangulation of the graph associated with the model. Some numerical experiments demonstrate the competitive performance of the proposed algorithm.

Keywords:

• Chordal extension
• Chordal graph
• Covariance selection
• Maximum likelihood

Mathematics Subject Classification:

• 62H12
• 62H20

Acknowledgment

The authors are grateful to two anonymous referees for constructive comments and suggestions, which have led to improvements in the presentation of the article.